docs: def choynowski(e, b, n, threshold=None):
      """Choynowski map probabilities [Choynowski1959]_ .
  
      Parameters
      ----------
      e          : array(n*h, 1)
                   event variable measured for each age group across n spatial units
      b          : array(n*h, 1)
                   population at risk variable measured for each age group across n spatial units
      n          : integer
                   the number of spatial units
      threshold  : float
                   Returns zero for any p-value greater than threshold
  
      Notes
      -----
      e and b are arranged in the same order
  
      Returns
      -------
                 : array (nx1)
  
      Examples
      --------
  
      Creating an array of an event variable (e.g., the number of cancer patients)
      for 2 regions in each of which 4 age groups are available.
      The first 4 values are event values for 4 age groups in the region 1,
      and the next 4 values are for 4 age groups in the region 2.
  
      >>> e = np.array([30, 25, 25, 15, 33, 21, 30, 20])
  
      Creating another array of a population-at-risk variable (e.g., total population)
      for the same two regions.
      The order for entering values is the same as the case of e.
  
      >>> b = np.array([100, 100, 110, 90, 100, 90, 110, 90])
  
      Specifying the number of regions.
  
      >>> n = 2
  
      Applying indirect_age_standardization function to e and b
  
      >>> a,b = choynowski(e, b, n)
      >>> round(a, 3)
      0.304
      >>> round(b, 3)
      0.294
  
      """
      e_by_n = sum_by_n(e, 1.0, n)
      b_by_n = sum_by_n(b, 1.0, n)
      r_by_n = sum(e_by_n) * 1.0 / sum(b_by_n)
      expected = r_by_n * b_by_n
      p = []
      for index, i in enumerate(e_by_n):
          if i <= expected[index]:
              p.append(poisson.cdf(i, expected[index]))
          else:
              p.append(1 - poisson.cdf(i - 1, expected[index]))
      if threshold:
          p = [i if i < threshold else 0.0 for i in p]
      return np.array(p)